System and Method for Adaptive Motion Sensing with Location Determination

ABSTRACT

A system and method for adaptive motion sensing with location determination is described. In one embodiment, the adaptive motion sensor is based on vibration sensor readings and can identify different states of motions based on modifiable parameters.

This application is a continuation of non-provisional patent applicationSer. No. 13/052,433 (Now U.S. Pat. No. 9,128,179), filed Mar. 21, 2011,which is a continuation of non-provisional patent application Ser. No.12/349,031 (Now U.S. Pat. No. 7,911,329), filed Jan. 6, 2009, which is acontinuation of non-provisional patent application Ser. No. 11/377,653(Now U.S. Pat. No. 7,486,174), filed Mar. 17, 2006. Non-provisionalapplication Ser. No. 11/377,653 claims priority to provisionalapplication no. 60/715,592, filed Sep. 12, 2005, and provisionalapplication no. 60/750,791, filed Dec. 16, 2005. Each of theabove-identified applications is incorporated herein by reference in itsentirety.

BACKGROUND

1. Field of the Invention

The present invention relates generally to monitoring and tracking and,more particularly, to a system and method for adaptive motion sensingwith location determination.

2. Introduction

Tracking mobile assets represents a growing enterprise as companies seekincreased visibility into the status of a service fleet (e.g., long-hauldelivery fleet). Visibility into the status of a service fleet can begained through mobile terminals that are affixed to service vehicles.These mobile terminals can be designed to generate position informationthat can be used to update status reports that are provided to customerrepresentatives.

In generating status reports to a centralized facility, the mobileterminal can generate position information through the reception ofsatellite position signals such as that generated by the GPS satellitenetwork. Processing these GPS signals, generating position information,and transmitting status reports to the centralized facility comes at theexpense of the power requirements at the mobile terminal. Here, anincreased number of reporting cycles reduces the effective battery lifeof the mobile terminal, thereby increasing the maintenance and fieldcosts of the mobile terminals. Thus, what is needed is a system andmethod for increasing visibility into the mobile assets, whilemaintaining a reasonable battery life of the mobile terminal.

SUMMARY

The present invention meets the above-mentioned needs by providing anadaptive motion sensor system on a transport vehicle. In one embodiment,the adaptive motion sensor is based on vibration sensor readings and canidentify different states of motion based on modifiable parameters.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will become more fullyapparent from the following description and appended claims, or may belearned by practice of the invention as set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings in which:

FIG. 1 illustrates an embodiment of a satellite network in communicationwith a mobile terminal.

FIGS. 2A and 2B illustrate an example of a timeline of status reportsgenerated by a moving asset.

FIG. 3 illustrates an embodiment of an adaptive motion sensor system.

FIG. 4 illustrates an example of accelerometer data.

FIG. 5 illustrates results of filtering on raw accelerometer data.

FIG. 6 illustrates an example of different motion states.

FIG. 7 illustrates an example of integration to a motion state.

FIG. 8 illustrates an example of histogram data.

FIG. 9 illustrates a block diagram of an embodiment of an adaptivethreshold process.

FIG. 10 illustrates an example of a match-filtered histogram.

DETAILED DESCRIPTION

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.

In accordance with the present invention, a system and method isprovided that enables the acquisition and sending of asset positioninformation, start times, and stop times on an on-demand or event drivenbasis. One example of an event is when a mobile asset either starts orstops moving. It is a feature of the present invention that thistracking mechanism provides the most useful and valuable informationabout the movement of an asset to the customer, and at the same timeminimizes the amount of power and bandwidth required, thereby reducingcost and extending battery life of the mobile terminal.

To appreciate the advantages of the present invention, it should berecognized that there is a difference between “data” and “information”.Data is used to send information. Data can therefore be sent with orwithout any information in it. Energy or power is required to collectdata. Bandwidth and cost is required to send data over a medium such asa satellite network. Ideally, data is collected and sent only when itcontains information.

For asset tracking, the information can include the change of positionand the time in which it occurred. If a position does not change, thereis no new information, and no need to collect and send more data. Oneexample of this is when a trailer is parked for three weeks in a yard.Here, a regularly scheduled reporting rate of once per day will have onereport with information, and 20 reports with redundant data (or noinformation) since the position or stop time hasn't changed. Thisexample illustrates a tremendous waste of power, bandwidth and cost.This waste can be eliminated by detecting a stop event, then collectingand sending position and stop-time information a single time. There isno need to collect and send data again until the position changes or theasset starts moving. When the asset does start moving, the positionchanges and new information can then be sent. While moving, the positioncontinually changes, and the need for position reports can drive thefrequency of further updates. Many long-haul fleets are interested inpickup and delivery only, and not location in route. If positioninformation is desired in route, the asset can either be paged forposition, or can be given a temporary scheduled reporting rate (e.g.,every 2 hours) over-the-air to leave a trail to track the asset inroute. When the asset stops, the temporary scheduled reporting rate canbe removed or lowered.

The quality of service from on-demand reporting is superior toconventional once per day reporting. The problem with once-per-dayreports is that the information can be almost 24 hours old when it isretrieved. Typically, customers want reports around the same time of dayor during “prime time.” Prime time for dispatch trailer tracking is inthe morning between the hours of 4AM to 10AM. When dispatchers or otherusers arrive at work in the morning, they want a recent position ofwhere the trailers are, typically within a few hours. Like othernetworks, including cellular phone networks, everyone cannot use thenetwork at the same time. Either the users accept older positions, orthe service company expands the size of the network, which becomes verycost ineffective. The solution to satisfy the user and the servicecompany is to use on-demand reporting.

With on-demand reporting, the user is satisfied because at any time ofday, morning or night, they can know where their trailers are withinminutes most of the time. This results because trailers are stopped mostof the time, much more than they are moving. When a dispatcher looks atthe position of an asset that stopped two weeks ago, they immediatelyknow where it is at the moment they look. That's because it is stopped.Otherwise, if it moved from that location, it would have sent an eventindicating it started to move. In the case where the asset is movingwhen an inquiry is made, the position could be hours old or as long asthe unit keeps moving without stopping. If a two-hour reporting rate isapplied while moving, then the dispatcher knows where the asset iswithin two hours, and on average, within 1 hour. This is far superiorthen once per day position reporting.

To the service provider, on-demand service is easier to provide thanscheduled reporting during prime time. This is based on the fact thatmost long-haul trucking companies operate at all times of the day, wheretheir assets start and stop at all different times of day, andsubsequently, position and start/stop time information will be sent atall different times of day, spreading the network usage out over thewhole day and not just during prime time. Even with companies thatoperate in the daytime, the network usage will still be spread, andutilized more efficiently than scheduled reporting.

In accordance with the present invention, the mobile terminal of thepresent invention includes an adaptive motion sensor that is used todetect movement of assets and initiate GPS signal measurements forposition determination. The adaptive motion sensor also aids in thedetermination of arrival and departure times.

In one embodiment, the adaptive motion sensor is an independentprocessing unit within the mobile terminal and is capable ofimplementing adaptive processing in software. This adaptive processingautomatically adjusts thresholds used to determine whether an asset ismoving or not moving. Normally these thresholds are fixed and requiremanual adjustment for different asset types since each asset type hasdifferent characteristics (e.g., levels of vibration) while it is inmotion. For example, truck trailers ride rougher and vibrate at higherlevels as compared to cars, which ride smoother and vibrate at lowerlevels. Cars would therefore require a lower threshold than the trucktrailer to detect the vibration.

In one vibration sensor embodiment, three valid states can be defined:(1) no vibration where the engine is off and no movement; (2) engine onbut no movement; and (3) engine on and movement. The adaptive processingcan collect and process vibration data to determine the levels ofvibration for each state and automatically adjust a threshold todetermine whether the asset is moving or not. Automatically oradaptively determining this threshold alleviates a large amount ofeffort required to determine this threshold manually. Not only will thisreduce effort and cost and make the use of motion sensors more scalable,but it will also improve the reliability and performance of the motiondetection processing since it can find the optimum thresholdsautomatically.

Prior to describing the details of a mobile terminal incorporating anadaptive motion sensor system, a description of an embodiment of anoperational context in which the mobile terminal can operate is firstprovided. FIG. 1 illustrates an embodiment of a satellite network thatincludes operations gateway 102, communicating with satellite gateway104, and has one forward and one return link (frequency) over satellite106 to mobile terminal 120 located on the asset. The satellite waveformis implemented in the Time Division Multiple Access (TDMA) structure,which consists of 57600 time slots each day, per frequency or link,where each slot is 1.5 seconds long. On the forward link, operationsgateway 102 sends a message or packet to mobile terminal 120 on one ofthe 1.5 second slots to give instructions to global locating system(GLS) component 124 via satellite modem processor 122. One example is toinstruct GLS component 124 to perform a GPS collection (e.g., code phasemeasurements) and transmit the data back to operations gateway 102. WhenGLS component 124 of mobile terminal 120 receives this forward command,it collects the GPS information and transmits the data back on thereturn link, on the same slot, delayed by a fixed time defined by thenetwork. The delay is needed to decode the forward packet, perform theGPS collect and processing, and build and transmit the return packet.

From there, operations gateway 102 passes the information to operationcenter 112, where the information is used to solve for position andpresent the position information to the customer via the internet. Adetailed description of this process is provided in U.S. Pat. No.6,725,158, entitled “System and Method for Fast Acquisition PositionReporting Using Communication Satellite Range Measurement,” whichincorporated herein by reference in its entirety.

It should be noted that the principles of the present invention can alsobe applied to other satellite-based or terrestrial-based locationdetermination systems where the position is determined at the mobileterminal independently, or at the mobile terminal in combination withinformation received from another location.

As illustrated in FIG. 1, mobile terminal 120 also includes adaptivemotion sensor 126. The main task of adaptive motion sensor 126 is todetermine whether an asset is moving or not. From there, together withthe mobile terminal processor (not shown) and GLS component 124 it candetermine the arrival and departure times and locations of an asset.When an asset begins to move, the adaptive motion sensor 126 detects themotion or vibration and sends a signal to the mobile terminal processorinforming it that motion has started. The mobile terminal processor thenrecords the time motion started, and signals to GLS component 124 tocollect code phase. The start time and the codephase are sent over thesatellite back to operations gateway 102 and operation center 112 wherethe codephase is used to solve for position, and the start time is usedto generate the departure time. Conversely, when adaptive motion sensor126 determines motion has stopped it will again inform the mobileterminal processor to collect time and codephase, and send theinformation back to operations gateway 102. Operation center 112 solvesfor position, and the stop time is used to generate the arrival time.The arrival and departure times along with their locations can besupplied to the user via the Internet. As noted, in an alternativeembodiment, the mobile terminal could send a position determined at themobile terminal back to operations center 112.

In one embodiment, adaptive motion sensor 126 has a layer of filteringthat is capable of filtering out unwanted starts and stops and onlytransmits true arrival and departure information. Adaptive motion sensor126 can be configured to only transmit starts or stops when the changein motion is maintained for a configurable percentage of time. In thismanner, only accurate arrival and departure time information istransmitted using the mobile terminal with the adaptive motion sensor.This layer of filtering saves on unwanted transmissions, and hencepower, bandwidth, and cost.

In one embodiment, mobile terminal 120 is configured to transmit aposition report after the actual arrival or departure times when themotion sensor has reached its “no-motion” or “motion” times,respectively. The “motion” and “no-motion” times can be separatelyconfigurable, for example, from one minute up to two hours. Thisconfigurability can be used to allow more time to exit an area ofinterest, or allow more time at rest stops along the way.

In one embodiment, the user-configurable “motion sensitivity” can beimplemented as the percentage of time the asset needs to remain inmotion during the “motion time” to signal motion. This is useful, forexample, in maintaining a motion condition while stopped at a trafficlight or a rest stop. Conversely, the user-configurable “no-motionsensitivity” can be implemented as the percentage of time the assetneeds to remain in no-motion during the “no-motion” time to signalno-motion. This is useful, for example, in maintaining a no-motioncondition while moving a trailer within a yard.

FIGS. 2A and 2B illustrate an example of a timeline of a unit movingfrom point A to point E, and stopping in between. In this example, twostates are used for the adaptive motion sensor: motion and no-motion.The user-configurable motion time is set at 15 minutes, while theuser-configurable motion sensitivity is set at 70%. Theuser-configurable no-motion time is set at 30 minutes, while theuser-configurable no-motion sensitivity is set at 70%.

The timeline begins at 10AM when the asset begins to leave a yard atpoint A on its trip to point E. When the adaptive motion sensordetermines a transition to the motion state, it records the time of10AM. The asset then stops at a traffic light between point A and pointB for three minutes. During this time, the adaptive motion sensordetermines that the asset is in a no-motion condition for those threeminutes. It should be noted that even with the existence of the motioncondition prior to the traffic light stop, the mobile terminal does notreport that the asset has departed point A. This results because theuser-configurable motion time has been set at 15 minutes. Thus, themotion time threshold has not yet been reached. When the 15-minutemotion time has expired, the mobile terminal then determines whether theuser-configurable motion sensitivity has been satisfied. With a motionsensitivity of 70%, the asset would need to maintain a motion conditionfor at least 70% of the 15 minutes, or 10.5 minutes. In this example,the asset has maintained a motion condition for 12 of the 15 minutes,therefore satisfying the motion sensitivity threshold. With both thetime and sensitivity thresholds being met, the mobile terminal thentransmits a message to the operations center that the asset has departedpoint A at 10AM. The time of transmission is illustrated as point B.Here, it should be noted that the time reported (i.e., 10AM) is not thesame as the time of the report (i.e., 10:15AM).

After the transmission at point B, the asset stops at a rest stop for 15minutes. This 15-minute stop does not trigger an arrival message becauseit has not met the user-configurable no-motion time and sensitivityparameters of 30 minutes and 70%, respectively. Specifically, the15-minute stop has not met the 21 minute (i.e., 70% of 30 minutes)threshold dictated by the user-configurable no-motion parameters.

At 12AM the asset stops at point C in a yard. Even with therepositioning of the asset within the yard for about 5 minutes, theadaptive motion sensor determines that the asset has maintained ano-motion condition for more than 70% of the 30 minutes. At theexpiration of the no-motion time, the mobile terminal then transmits amessage at 12:30AM indicating that the asset had stopped at 12AM.

At 3PM, the adaptive motion sensor determines that the asset has entereda motion condition as the asset resumes its journey. At 3:15PM, theuser-configurable motion time and sensitivity parameters are met and themobile terminal then transmits a message at 3:15PM indicating that theasset has departed at 3PM.

This process continues as the asset continues on to point E. Throughoutthis process, the mobile terminal transmits start and stop messages onlywhen the user-configurable time and sensitivity parameters are met. Inone embodiment, the mobile terminal can also be configured toperiodically transmit status reports (e.g., once per hour) when in amotion condition. These periodic status reports would enable the systemto track the asset while en route.

Arrival times, departures times, and code phase collections areinitiated by the adaptive motion sensor when the asset starts and stopsmoving. In one embodiment, detection of when an asset starts and stopsmoving is based on the change in measurable vibration on the asset thatis caused when an asset starts or stops moving. The adaptive motionsensor can therefore be designed to measure the amount of vibration oracceleration to determine movement. Complications can arise whenvibration or acceleration is caused by other extraneous factors such asan engine running, or a compressor or refrigeration unit running Thevibration from the other sources can be detected by the sensor and cancause false indications. The adaptive motion sensor can be designed todifferentiate between vibration resulting from true movement andvibration resulting from extraneous sources. If it is assumed thatmovement must come from a vehicle, and that the vehicle cannot moveunless an engine is running, then three states of motion can be defined:(1) engine off, no movement; (2) engine on, no movement; and (3) engineon, moving. There are other possible states such as engine off andmovement, but not valid. Also, state (2) may in fact have two or moreindividual states from separate engines or motors such as refrigerationunits and compressors.

For simplicity, vibration from one or more engines can be treated as onestate. These three states will produce three distinct levels ofvibration in which the motion sensor can use to determine movement. Todetermine these states the adaptive motion sensor can collect andprocess data from a vibration sensor.

FIG. 3 illustrates an embodiment of a system that converts vibrationinto a usable filtered number, which can be used to determine the stateof motion (e.g., moving or not-moving). In one embodiment, vibrationsensor 302 produces a voltage that is proportional to the amount ofacceleration or vibration. One such device is an accelerometer-basedMEMS (Micro-Electro-Mechanical System) device, which can detectacceleration in two or three axis. For detecting vibration, 2-axis isusually adequate.

Voltage from the accelerometer is then fed into A/D converter 304. Theoutput of A/D converter 304 produces a number that is proportional tothe amount of acceleration or vibration measured by sensor 302. Alow-speed A/D converter can be used to convert a low-bandwidth (e.g.,less than 50 hz) signal from an analog voltage to a digital value. Thesystem can be designed to sample A/D converter 304 for a very short timeat a very slow rate (e.g., measure for a few milliseconds every fiveseconds) to operate as an ultra-low power device. FIG. 4 shows exampledata from a three-axis (x, y and z) accelerometer on an asset throughvarious states of movement from moving on the highway to no motion. Thevalues shown are the difference or derivative from consecutive samples.

An accelerometer sensor detects acceleration on each of its axisincluding that caused by gravity. The result is a constant DC voltagefrom the axis that is affected by gravity. To detect acceleration onlyfrom vibration and not gravity, the difference or derivative is takenbetween consecutive samples to remove the DC values and the effect fromgravity and tilting of the sensor.

Vibration filter 306 smoothes the readings produced by A/D converter 304to reduce the variance from successive samples. Raw A/D samples areprocessed in vibration filter 306 to produce a smoother numeric valuerepresenting the level of vibration. In one embodiment, a sample istaken every five seconds on each axis of the accelerometer. The delta ordifference between the new sample and the last sample is then taken fromthe corresponding axis. The deltas are integrated over six samples orevery 30 seconds to produce the filtered vibration value. Integration ofthe delta over six samples has been found to have the most sensitivityto vibration over other means of filtering such as a moving filter orIIR filter. The chart in FIG. 5 shows the results from the differentfiltering techniques from the raw data shown in FIG. 4. The simple delta(in red) produced the largest and most usable filtered vibration values.

Filtered vibration values are fed into adaptive threshold stage 308 andmotion detection stage 310. Based on the input configuration parameters,motion detection stage 310 performs a second level of filtering todetermine the motion state. In one embodiment, a motion state does notchange unless the new motion state is maintained for a configuredpercentage of time. This assists in filtering out momentary or temporarychanges in motion state. Motion detection stage 310 compares thefiltered vibration value at its input to a threshold to determine thecurrent sampled motion state. If the new filtered vibration values areabove the threshold, motion detection stage 310 interprets the newreading as “motion” and conversely, if below the threshold interpretsthe new reading as “no-motion.” It will then process these new raw inputvalues through the second stage filter to determine the current motionstate.

In one embodiment motion detection stage 310 is implemented in software,which uses the filtered vibration inputs to determine the current stateof motion. FIG. 6 illustrates an example of the various states of motiondetection stage 310.

On initial power up the motion detector is in an unknown state. Thestates are changed when motion detection stage 310 determines thatcriteria have been met for a motion or no-motion state. The criteria isbased on the filtered vibration input value, the vibration threshold,the motion or no-motion times, and their corresponding percentage.

To determine the current or next state, motion detection stage 310samples the filtered vibration input value at a uniform rate andcompares it to the vibration threshold. If the value is above thethreshold it will add “+1” to a motion integrator. Conversely, if thevalue is below the vibration threshold, it will add “−1”, or subtract 1from the motion integrator. If the motion integrator integrates up to apositive threshold called the motion integration threshold, it changesthe state to “motion”. Conversely, if the motion integrator integratesdown to a negative threshold called the no-motion integration threshold,it changes the state to “no-motion”. From the “Unknown” state motiondetection stage 310 integrates values until it reaches either the motionor no-motion integration threshold. FIG. 7 illustrates an example ofintegration to a motion state.

As noted, the motion or no-motion integration thresholds are based on astart time and stop time, and a start sensitivity and stop sensitivity,respectively. These over-the-air configurable parameters allow a user tospecify what motion or no-motion means in their own particular context.For example, a user can specify that to change from a no-motion state toa motion state, the asset must be in motion (moving) for at least 15minutes, 70% of the time. This means from the start of motion, thefiltered vibration values must stay above the threshold for the next 15minutes 70% of the time or for a total time of 15*0.7=10.5 minutes. Thisallows a unit to survive brief stops such as at a traffic light after ithas truly started motion. For this example, when motion starts, a timeralso starts. When the timer reaches 15 minutes, if the integrated valueis above the threshold, the state will change to motion. The integratedvalue is only reached if the unit stayed in motion for a total of 10.5minutes.

Adaptive threshold stage 308 inputs the same filtered vibration valuesas motion detection stage 310 to automatically adjust the thresholdvalue to an optimum value for determining the difference between“motion” and “no-motion.” Adaptive threshold stage 308 enhances theperformance of motion detection stage 310 and eliminates the need formanual adjustment of the vibration threshold. This results because thevibration characteristics can vary from asset to asset in which themobile terminal and the adaptive motion sensor are installed. Also, thesensors themselves, such as an accelerometer, can vary in sensitivity.For these reasons, the vibration threshold may need to be different foreach sensor and asset for optimum performance. To avoid having tomanually adjust thresholds, adaptive threshold stage 308 collectsinformation about the vibration characteristics and uses thisinformation to automatically adjust the vibration threshold to anoptimum level for the particular sensor and asset.

As noted, in one embodiment, three valid states of motion for an assetcan be defined: (1) engine off, no movement; (2) engine on, no movement;and (3) engine on, moving. Each of these three states produces threedistinct levels of the filtered vibration values. Collected data candetermine what these different levels are and can be used to adjust thevibration threshold. In one embodiment, the filtered vibration valuesare used to generate a histogram.

FIG. 8 illustrates an example of a histogram generated from the filteredvibration values from a motion sensor on an asset over a period of timein which the asset has had many starts and stops. State 1 data has beenzeroed out since this data is not useful for adjusting the threshold.Also, this data would dominate the histogram since assets are typicallynot moving or stopped most of the time. Essentially, the two usefulstates for adjusting the threshold are states 2 and 3. To find thestates that distinguish motion and no-motion, the peaks are identifiedfrom right to left, or from higher to lower vibration. This first peakfrom the right represents the average level of vibration for an asset instate 3 (i.e., moving with engine or engines on). The next lowest peak(to the left) in the histogram corresponds to state 2 (i.e., engine on,not moving). Ideally, the vibration threshold should lie at the nullbetween the histogram peaks for states 2 and 3.

To find a good midpoint, the histogram is processed to find the peaks.FIG. 9 illustrates an embodiment of the adaptive threshold process. Atstep 902, the histogram is updated. Here, filtered vibration values havea fixed range based on the gain from the accelerometer sensor and thesize of the A/D. The maximum established range of the filtered vibrationvalues can then be divided into X number of ranges or bins of thehistogram. As each filtered vibration value enters this stage, thecorresponding bin or range is incremented. One bin is incremented foreach new filtered vibration value. To prevent overflow, the histogramscans all bins for the highest peak. If an identified peak value is oneincrement away from the maximum numeric bin value (e.g. 255 for 8-bits),then all bins can be scaled down by two to prevent overflow. Thisessentially changes the histogram from an integrator to a recursivefilter for each bin. This means that the histogram has a limited memoryby retaining only the most recent values, and can change or evolve asvibration characteristic change or evolve.

Typically, assets and the adaptive motion sensor, are stopped most ofthe time or are in state 1. Most of the filtered vibration values fallin the lower bins of the histogram creating a large peak. Since thisinformation is not useful for adjusting the threshold it is filtered outto prevent it from dominating the histogram. A configurable vibrationlimit can be used to specify the minimum filtered vibration that can beused in the histogram. By doing this the histogram will only containdata from state 2 and state 3. This data contains the information neededto adjust the vibration threshold.

At step 904, the histogram is match filtered. It should be noted,however, that before match filtering the raw histogram, there should bea sufficient amount of data in the histogram. The criteria to continuecan be simply based on having a specified minimum number of data pointsavailable. For example, the sum of all the values in the histogram(energy) must exceed a specified minimum value. This is better thansimply testing the max value of any bin.

Since the histogram may not have a smooth shape, some form of filtering(e.g., match filtering) can be used to help find the true peak. Matchfilters can work well when there is a know pattern in the presence ofnoise. The known pattern in this case is one caused by a constantvibration while in motion. This pattern will look like a bell curvewhere the center is the average vibration value. Momentary variations invibration levels are cause by random positions from the sensor, and fromchanges from the source of vibration such as the road, the engine, etc.Over a long period of time the vibration levels should average out to abell curve. The match filter coefficients can be modeled after the bellcurve produced under constant vibration from motion over a long periodof time.

FIG. 10 illustrates the raw histogram with the match-filtered versionbelow. The idea here is to center the filter over each bin and multiplyeach point of the filter with each of the corresponding bins about thecenter to get the filtered point for that bin. The filter width can belimited to the significant information for each peak. The resultingmatch filtered output is smooth and should contain only one peak foreach vibration state.

At step 906, the peaks and nulls are found. This stage processes thematch-filtered histogram for the bin locations where the state 2 andstate 3 peaks are located as well as the bin where the null or low pointis between the two peaks. This information is sent to the next stage toadjust the vibration threshold to the ideal or optimum location at thenull between the two peaks. The null is where both bell curves, one fromstate 2 (engine on, no movement) and one from state 3 (engine on,moving), overlap. This is the location where the threshold will have thehighest probability of accurately distinguishing between moving andnot-moving.

At step 908, the threshold is updated. For each iteration of theadaptive threshold process, the vibration threshold to motion detectionstage 310 is updated. The new threshold uses the bin location of thenull and the previous filtered value to produce the new value using anIIR filter. The bin location of the state 2 and state 3 peaks are notused to update the threshold, rather they are used to qualify the resultto ensure it does not track to an erroneous value. The new threshold canbe calculated using the following equation: y(n)=k*y(n−1) +(1−k)*x(n)where, 0<=k<=1, k is the IIR filter coefficient, x(n) is the binlocation of the null after processing a new match filtered histogram,y(n−1) is the previous vibration threshold (in unit of bin numbers), andy(n) is the new vibration threshold.

In one embodiment, before updating the vibration threshold, a series ofqualification tests can be made using configurable parameters. Each ofthese checks or qualifiers should be passed in order to update thevibration threshold. In addition to the limits, each of thesequalification tests can be enabled or disabled independentlyover-the-air to provide maximum flexibility in adjusting the algorithmover the air. A “Master Fail” bit will be set if any of these enabledqualifiers fail. This bit can be sent over the air to allow a quicktally of all units that are not operating under normal parameters. Fromthere, the failing unit can be polled to extract the details of thefailure which include which qualifier failed, the match filteredhistogram, vibration threshold, etc. The intent of the qualifiers is toensure that the adaptive threshold process produces a threshold thatimproves performance and does not degrade it. It is better to falselyfail a qualifier and stop threshold adjustments than to adjust athreshold based on incorrect data, and degrade performance.

These and other aspects of the present invention will become apparent tothose skilled in the art by a review of the preceding detaileddescription. Although a number of salient features of the presentinvention have been described above, the invention is capable of otherembodiments and of being practiced and carried out in various ways thatwould be apparent to one of ordinary skill in the art after reading thedisclosed invention, therefore the above description should not beconsidered to be exclusive of these other embodiments. Also, it is to beunderstood that the phraseology and terminology employed herein are forthe purposes of description and should not be regarded as limiting.

What is claimed is:
 1. A system, comprising: a vibration sensor thatdetects vibrations from a transport vehicle; a detection component thatreceives first input values based on first readings of the vibrationsensor, the detection component comparing the received first inputvalues to a first threshold value and determining that the transportvehicle is in a first operating state when one or more received firstinput values are above the first threshold value and determining thatthe transport vehicle is in a second operating state when one or morereceived first input values are below the first threshold value; and anadaptive threshold component that identifies a second threshold value asa replacement for the first threshold value, the motion detectioncomponent comparing received second input values based on secondreadings of the vibration sensor to the second threshold value anddetermining that the transport vehicle is in the first operating statewhen one or more received second input values are above the secondthreshold value and determining that the transport vehicle is in thesecond operating state when one or more received second input values arebelow the second threshold value.
 2. The system of claim 1, wherein thefirst operating state is an engine on with movement operating state andthe second operating state is an engine on without movement operatingstate.
 3. The system of claim 1, wherein the vibration sensor is anaccelerometer based micro-electro-mechanical system.
 4. The system ofclaim 1, comprising an analog-to-digital converter coupled to thevibration sensor and a vibration filter that smoothes values produced bythe analog-to-digital converter.
 5. The system of claim 1, wherein theadaptive threshold component identifies the second threshold componentusing the first input values based on the first readings of thevibration sensor.
 6. The system of claim 1, wherein parameters of theadaptive threshold component are configurable using data provided byexternal communication received by the system.
 7. The system of claim 6,wherein the external communication is a satellite communication.
 8. Thesystem of claim 6, wherein the data provided by external communicationcontrols one or more qualification tests that determine whether theadaptive threshold component can update the first threshold value to thesecond threshold value.
 9. The system of claim 8, wherein the dataprovided by external communications enables or disables the one or morequalification tests.
 10. The system of claim 8, further comprising atransmitter that transmits information enabling indication of a failureof at least one of the one or more qualification tests.
 11. The systemof claim 1, wherein the transport vehicle is a tractor trailer.
 12. Amethod, comprising: generating, by a vibration sensor, first vibrationvalues based on first vibrations of a transport vehicle; comparing, by adetection component, the first vibration values to a first thresholdvalue and determining that the transport vehicle is in a first operatingstate when one or more of the first vibration values are above the firstthreshold value and determining that the transport vehicle is in asecond operating state when one or more of the first vibration valuesare below the first threshold value; identifying, by an adaptivethreshold component, a second threshold value as a replacement for thefirst threshold value; generating, by the vibration sensor, secondvibration values based on second vibrations of the transport vehicle;and comparing, by the detection component, the second vibration valuesto the second threshold value and determining that the transport vehicleis in the first operating state when one or more of the second vibrationvalues are above the second threshold value and determining that thetransport vehicle is in the second operating state when one or more ofthe second vibration values are below the second threshold value. 13.The method of claim 12, wherein the first operating state is an engineon with movement operating state and the second operating state is anengine on without movement operating state.
 14. The method of claim 12,wherein the vibration sensor is an accelerometer basedmicro-electro-mechanical system.
 15. The method of claim 12, wherein theidentifying comprises identifying the second threshold value using thefirst vibration values.
 16. The method of claim 15, wherein theidentifying comprises determining the second threshold value based onmatch filtering of a raw histogram of the first vibration values. 17.The method of claim 12, further comprising: receiving data via satellitecommunication; and configuring parameters of the adaptive thresholdcomponent using the received data.
 18. The method of claim 17, whereinthe received data controls one or more qualification tests thatdetermine whether the adaptive threshold component can update the firstthreshold value to the second threshold value.
 19. The method of claim18, wherein the received data enables or disables the one or morequalification tests.
 20. The method of claim 18, further comprisingtransmitting, via satellite communication, information enablingindication of a failure of at least one of the one or more qualificationtests.